The present technique relates to the field of data processing. More particularly, it relates to scheduling of operations to be executed by a data processing apparatus.
Energy efficiency is becoming increasingly important for data processing apparatuses, such as mobile devices powered by batteries or wearable devices such as watches or glasses. Also, devices with relatively small processing capability are increasingly being included in objects within buildings and the outdoor environment which are connected to the “cloud” or “Internet of Things”. For example, sensors for sensing temperature or other conditions, health care monitoring devices for detecting health data for a user such as heart rate or blood pressure, or devices for controlling a lock on a door or a heating/air conditioning system, may be connected to the cloud so that data can be harvested and analyzed at a remote location, or a user can control their devices from a remote location. For such devices there may be relatively few tasks which the devices need to perform, and the device may not need to be active very often, and so energy efficiency may be a more important consideration than processing performance. Therefore, the present technique seeks to provide a more energy efficient approach for performing operations using a data processing apparatus.